Factor Models with Sparse VAR Idiosyncratic Components
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Matteo Barigozzi & Christian Brownlees, 2019.
"NETS: Network estimation for time series,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(3), pages 347-364, April.
- Matteo Barigozzi & Christian Brownlees, 2013. "Nets: Network Estimation for Time Series," Working Papers 723, Barcelona School of Economics.
- Barigozzi, Matteo & Brownlees, Christian T., 2018. "Nets: network estimation for time series," LSE Research Online Documents on Economics 90493, London School of Economics and Political Science, LSE Library.
- Matteo Barigozzi & Christian T. Brownlees, 2013. "Nets: Network estimation for time series," Economics Working Papers 1391, Department of Economics and Business, Universitat Pompeu Fabra.
- Choi, In, 2012.
"Efficient Estimation Of Factor Models,"
Econometric Theory, Cambridge University Press, vol. 28(2), pages 274-308, April.
- In Choi, 2007. "Efficient Estimation of Factor Models," Working Papers 0701, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy), revised Dec 2010.
- Hansheng Wang & Bo Li & Chenlei Leng, 2009. "Shrinkage tuning parameter selection with a diverging number of parameters," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(3), pages 671-683, June.
- S. Boragan Aruoba & Francis X. Diebold, 2010.
"Real-Time Macroeconomic Monitoring: Real Activity, Inflation, and Interactions,"
American Economic Review, American Economic Association, vol. 100(2), pages 20-24, May.
- S. Boragan Aruoba & Francis X. Diebold, 2010. "Real-time macroeconomic monitoring: real activity, inflation, and interactions," Working Papers 10-5, Federal Reserve Bank of Philadelphia.
- S. Boragan Aruoba & Francis X. Diebold, 2010. "Real-Time Macroeconomic Monitoring: Real Activity, Inflation, and Interactions," NBER Working Papers 15657, National Bureau of Economic Research, Inc.
- S. Boragan Aruoba & Francis X. Diebold, 2010. "Real-Time Macroeconomic Monitoring: Real Activity, Inflation, and Interactions," PIER Working Paper Archive 10-002, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
- Smeekes, Stephan & Wijler, Etienne, 2018.
"Macroeconomic forecasting using penalized regression methods,"
International Journal of Forecasting, Elsevier, vol. 34(3), pages 408-430.
- Smeekes, Stephan & Wijler, Etiënne, 2016. "Macroeconomic Forecasting Using Penalized Regression Methods," Research Memorandum 039, Maastricht University, Graduate School of Business and Economics (GSBE).
- Alain Hecq & Luca Margaritella & Stephan Smeekes, 2023.
"Granger Causality Testing in High-Dimensional VARs: A Post-Double-Selection Procedure,"
Journal of Financial Econometrics, Oxford University Press, vol. 21(3), pages 915-958.
- Alain Hecq & Luca Margaritella & Stephan Smeekes, 2019. "Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection Procedure," Papers 1902.10991, arXiv.org, revised Dec 2020.
- Medeiros, Marcelo C. & Mendes, Eduardo F., 2016. "ℓ1-regularization of high-dimensional time-series models with non-Gaussian and heteroskedastic errors," Journal of Econometrics, Elsevier, vol. 191(1), pages 255-271.
- Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008.
"Nowcasting: The real-time informational content of macroeconomic data,"
Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
- Reichlin, Lucrezia & Giannone, Domenico & Small, David, 2005. "Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases," CEPR Discussion Papers 5178, C.E.P.R. Discussion Papers.
- Domenico Giannone & Lucrezia Reichlin & David H. Small, 2005. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Finance and Economics Discussion Series 2005-42, Board of Governors of the Federal Reserve System (U.S.).
- Domenico Giannone & Lucrezia Reichlin & David H Small, 2007. "Nowcasting GDP and Inflation: The Real-Time Informational Content of Macroeconomic Data Releases," Money Macro and Finance (MMF) Research Group Conference 2006 164, Money Macro and Finance Research Group.
- Jushan Bai & Serena Ng, 2020. "Simpler Proofs for Approximate Factor Models of Large Dimensions," Papers 2008.00254, arXiv.org.
- Greenaway-McGrevy, Ryan & Han, Chirok & Sul, Donggyu, 2012. "Estimating the number of common factors in serially dependent approximate factor models," Economics Letters, Elsevier, vol. 116(3), pages 531-534.
- Wu, Wei Biao & Zaffaroni, Paolo, 2018. "Asymptotic Theory For Spectral Density Estimates Of General Multivariate Time Series," Econometric Theory, Cambridge University Press, vol. 34(1), pages 1-22, February.
- Forni, Mario & Hallin, Marc & Lippi, Marco & Zaffaroni, Paolo, 2017.
"Dynamic factor models with infinite-dimensional factor space: Asymptotic analysis,"
Journal of Econometrics, Elsevier, vol. 199(1), pages 74-92.
- Pietro Dallari & Antonio Ribba, 2015. "Dynamic Factor Models with In nite-Dimensional Factor Space: Asymptotic Analysis," Center for Economic Research (RECent) 115, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
- Mario Forni & Marc Hallin & Marco Lippi & Paolo Zaffaroni, 2016. "Dynamic Factor Models with Infinite-Dimensional Factor Space. Asymptotic Analysis," EIEF Working Papers Series 1607, Einaudi Institute for Economics and Finance (EIEF), revised Apr 2016.
- Lippi, Marco & Hallin, Marc & Forni, Mario & Zaffaroni, Paolo, 2015. "Dynamic Factor Models with Infinite-Dimensional Factor Space: Asymptotic Analysis," CEPR Discussion Papers 10618, C.E.P.R. Discussion Papers.
- Mario Forni & Marc Hallin & Marco Lippi & Paolo Zaffaroni, 2015. "Dynamic Factor Models with Infinite-Dimensional Factor Space: Asymptotic Analysis," Working Papers ECARES ECARES 2015-23, ULB -- Universite Libre de Bruxelles.
- Blix, Mårten, 1999. "Forecasting Swedish Inflation With a Markov Switching VAR," Working Paper Series 76, Sveriges Riksbank (Central Bank of Sweden).
- Matteo Barigozzi & Marc Hallin, 2017.
"A network analysis of the volatility of high dimensional financial series,"
Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(3), pages 581-605, April.
- Barigozzi, Matteo & Hallin, Marc, 2017. "A network analysis of the volatility of high-dimensionalfinancial series," LSE Research Online Documents on Economics 67456, London School of Economics and Political Science, LSE Library.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022.
"How is machine learning useful for macroeconomic forecasting?,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2019. "How is Machine Learning Useful for Macroeconomic Forecasting?," CIRANO Working Papers 2019s-22, CIRANO.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & St'ephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Papers 2008.12477, arXiv.org.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Working Papers 20-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Aug 2020.
- Jushan Bai & Serena Ng, 2002.
"Determining the Number of Factors in Approximate Factor Models,"
Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
- Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Econometric Society World Congress 2000 Contributed Papers 1504, Econometric Society.
- Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Boston College Working Papers in Economics 440, Boston College Department of Economics.
- Kock, Anders Bredahl & Callot, Laurent, 2015.
"Oracle inequalities for high dimensional vector autoregressions,"
Journal of Econometrics, Elsevier, vol. 186(2), pages 325-344.
- Anders Bredahl Kock & Laurent A.F. Callot, 2012. "Oracle Inequalities for High Dimensional Vector Autoregressions," CREATES Research Papers 2012-16, Department of Economics and Business Economics, Aarhus University.
- Hecq Alain & Laurent Sébastien & Palm Franz C., 2016.
"On the Univariate Representation of BEKK Models with Common Factors,"
Journal of Time Series Econometrics, De Gruyter, vol. 8(2), pages 91-113, July.
- Hecq, A.W. & Palm, F.C. & Laurent, S.F.J.A., 2012. "On the univariate representation of BEKK models with common factors," Research Memorandum 018, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
- Alain Hecq & Franz C. Palm & Sébastien Laurent, 2016. "On the Univariate Representation of BEKK Models with Common Factors," Post-Print hal-01440307, HAL.
- Michael W. McCracken & Serena Ng, 2016.
"FRED-MD: A Monthly Database for Macroeconomic Research,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 574-589, October.
- Michael W. McCracken & Serena Ng, 2015. "FRED-MD: A Monthly Database for Macroeconomic Research," Working Papers 2015-12, Federal Reserve Bank of St. Louis.
- Sydney C. Ludvigson & Serena Ng, 2009. "A Factor Analysis of Bond Risk Premia," NBER Working Papers 15188, National Bureau of Economic Research, Inc.
- Bai, Jushan & Ng, Serena, 2019. "Rank regularized estimation of approximate factor models," Journal of Econometrics, Elsevier, vol. 212(1), pages 78-96.
- Jim Lee, 2013. "Business Cycle Synchronization in Europe: Evidence from a Dynamic Factor Model," International Economic Journal, Taylor & Francis Journals, vol. 27(3), pages 347-364, September.
- Forni, Mario, et al, 2001.
"Coincident and Leading Indicators for the Euro Area,"
Economic Journal, Royal Economic Society, vol. 111(471), pages 62-85, May.
- Lucrezia Reichlin & Mario Forni & Marc Hallin & Marco Lippi, 2001. "Coincident and leading indicators for the Euro area," ULB Institutional Repository 2013/10137, ULB -- Universite Libre de Bruxelles.
- Boivin, Jean & Ng, Serena, 2006.
"Are more data always better for factor analysis?,"
Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
- Jean Boivin & Serena Ng, 2003. "Are More Data Always Better for Factor Analysis?," NBER Working Papers 9829, National Bureau of Economic Research, Inc.
- Jianqing Fan & Ricardo Masini & Marcelo C. Medeiros, 2021. "Bridging factor and sparse models," Papers 2102.11341, arXiv.org, revised Sep 2022.
- Cai, Tony & Liu, Weidong, 2011. "Adaptive Thresholding for Sparse Covariance Matrix Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 672-684.
- Stock, James H. & Watson, Mark W., 1999.
"Forecasting inflation,"
Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
- James H. Stock & Mark W. Watson, 1999. "Forecasting Inflation," NBER Working Papers 7023, National Bureau of Economic Research, Inc.
- Li, Kathleen T. & Bell, David R., 2017. "Estimation of average treatment effects with panel data: Asymptotic theory and implementation," Journal of Econometrics, Elsevier, vol. 197(1), pages 65-75.
- Cheng Ju & David Benkeser & Mark J. van der Laan, 2020. "Robust inference on the average treatment effect using the outcome highly adaptive lasso," Biometrics, The International Biometric Society, vol. 76(1), pages 109-118, March.
- Marcelo C. Medeiros & Gabriel F. R. Vasconcelos & Álvaro Veiga & Eduardo Zilberman, 2021.
"Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 98-119, January.
- Marcelo Madeiros & Gabriel Vasconcelos & Álvaro Veiga & Eduardo Zilberman, 2019. "Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods," Working Papers Central Bank of Chile 834, Central Bank of Chile.
- Chang-Jin Kim & Charles R. Nelson, 1998. "Business Cycle Turning Points, A New Coincident Index, And Tests Of Duration Dependence Based On A Dynamic Factor Model With Regime Switching," The Review of Economics and Statistics, MIT Press, vol. 80(2), pages 188-201, May.
- Thomas J. Sargent & Christopher A. Sims, 1977.
"Business cycle modeling without pretending to have too much a priori economic theory,"
Working Papers
55, Federal Reserve Bank of Minneapolis.
- Tom Doan, "undated". "RATS program to estimate observable index model from Sargent-Sims(1977)," Statistical Software Components RTZ00126, Boston College Department of Economics.
- Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000.
"The Generalized Dynamic-Factor Model: Identification And Estimation,"
The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
- Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 1999. "The Generalized Dynamic Factor Model: Identification and Estimation," CEPR Discussion Papers 2338, C.E.P.R. Discussion Papers.
- Mario Forni & Marc Hallin & Lucrezia Reichlin & Marco Lippi, 2000. "The generalised dynamic factor model: identification and estimation," ULB Institutional Repository 2013/10143, ULB -- Universite Libre de Bruxelles.
- Massimiliano Marcellino & Mario Porqueddu & Fabrizio Venditti, 2016.
"Short-Term GDP Forecasting With a Mixed-Frequency Dynamic Factor Model With Stochastic Volatility,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(1), pages 118-127, January.
- Marcellino, Massimiliano & Venditti, Fabrizio & Porqueddu, Mario, 2013. "Short-term GDP forecasting with a mixed frequency dynamic factor model with stochastic volatility," CEPR Discussion Papers 9334, C.E.P.R. Discussion Papers.
- Massimiliano Marcellino & Mario Porqueddu & Fabrizio Venditti, 2013. "Short-term GDP forecasting with a mixed frequency dynamic factor model with stochastic volatility," Temi di discussione (Economic working papers) 896, Bank of Italy, Economic Research and International Relations Area.
- Zellner, Arnold & Palm, Franz, 1974.
"Time series analysis and simultaneous equation econometric models,"
Journal of Econometrics, Elsevier, vol. 2(1), pages 17-54, May.
- ZELLNER, Arnold & PALM, Franz, 1974. "Time series analysis and simultaneous equation econometric models," LIDAM Reprints CORE 173, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Koopman, Siem Jan & van der Wel, Michel, 2013.
"Forecasting the US term structure of interest rates using a macroeconomic smooth dynamic factor model,"
International Journal of Forecasting, Elsevier, vol. 29(4), pages 676-694.
- Siem Jan Koopman & Michel van der Wel, 2011. "Forecasting the U.S. Term Structure of Interest Rates using a Macroeconomic Smooth Dynamic Factor Model," Tinbergen Institute Discussion Papers 11-063/4, Tinbergen Institute.
- Johannes Tang Kristensen, 2013. "Diffusion Indexes with Sparse Loadings," CREATES Research Papers 2013-22, Department of Economics and Business Economics, Aarhus University.
- A. Belloni & D. Chen & V. Chernozhukov & C. Hansen, 2012.
"Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain,"
Econometrica, Econometric Society, vol. 80(6), pages 2369-2429, November.
- Alexandre Belloni & D. Chen & Victor Chernozhukov & Christian Hansen, 2010. "Sparse models and methods for optimal instruments with an application to eminent domain," CeMMAP working papers CWP31/10, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Alexandre Belloni & Daniel Chen & Victor Chernozhukov & Christian Hansen, 2010. "Sparse Models and Methods for Optimal Instruments with an Application to Eminent Domain," Papers 1010.4345, arXiv.org, revised Apr 2015.
- Fan, Jianqing & Ke, Yuan & Wang, Kaizheng, 2020. "Factor-adjusted regularized model selection," Journal of Econometrics, Elsevier, vol. 216(1), pages 71-85.
- Forni, Mario & Gambetti, Luca, 2010.
"The dynamic effects of monetary policy: A structural factor model approach,"
Journal of Monetary Economics, Elsevier, vol. 57(2), pages 203-216, March.
- Mario Forni & Luca Gambetti, 2008. "The dynamic e ects of monetary policy: A structural factor model approach," Center for Economic Research (RECent) 026, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
- Forni, Mario & Gambetti, Luca, 2008. "The Dynamic Effects of Monetary Policy: A Structural Factor Model Approach," CEPR Discussion Papers 7098, C.E.P.R. Discussion Papers.
- Onatski, Alexei, 2012. "Asymptotics of the principal components estimator of large factor models with weakly influential factors," Journal of Econometrics, Elsevier, vol. 168(2), pages 244-258.
- James H. Stock & Mark W. Watson, 1989.
"New Indexes of Coincident and Leading Economic Indicators,"
NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 351-409,
National Bureau of Economic Research, Inc.
- Stock, J.H. & Watson, M.W., 1989. "New Indexes Of Coincident And Leading Economic Indicators," Papers 178d, Harvard - J.F. Kennedy School of Government.
- Domenico Giannone & Lucrezia Reichlin & David Small, 2008. "Nowcasting: the real time informational content of macroeconomic data releases," ULB Institutional Repository 2013/6409, ULB -- Universite Libre de Bruxelles.
- Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
- Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2005.
"Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach,"
The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 120(1), pages 387-422.
- Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2004. "Measuring the effects of monetary policy: a factor-augmented vector autoregressive (FAVAR) approach," Finance and Economics Discussion Series 2004-03, Board of Governors of the Federal Reserve System (U.S.).
- Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2004. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," NBER Working Papers 10220, National Bureau of Economic Research, Inc.
- Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
- Wang, Hansheng & Leng, Chenlei, 2007. "Unified LASSO Estimation by Least Squares Approximation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1039-1048, September.
- Reichlin, Lucrezia & Giannone, Domenico & Small, David, 2005.
"Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases,"
CEPR Discussion Papers
5178, C.E.P.R. Discussion Papers.
- Giannone, Domenico & Reichlin, Lucrezia & Small, David H., 2006. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Working Paper Series 633, European Central Bank.
- Domenico Giannone & Lucrezia Reichlin & David H. Small, 2005. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Finance and Economics Discussion Series 2005-42, Board of Governors of the Federal Reserve System (U.S.).
- Domenico Giannone & Lucrezia Reichlin & David H Small, 2007. "Nowcasting GDP and Inflation: The Real-Time Informational Content of Macroeconomic Data Releases," Money Macro and Finance (MMF) Research Group Conference 2006 164, Money Macro and Finance Research Group.
- Alessi, Lucia & Barigozzi, Matteo & Capasso, Marco, 2010. "Improved penalization for determining the number of factors in approximate factor models," Statistics & Probability Letters, Elsevier, vol. 80(23-24), pages 1806-1813, December.
- Hallin, Marc & Liska, Roman, 2007. "Determining the Number of Factors in the General Dynamic Factor Model," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 603-617, June.
- Hansheng Wang & Runze Li & Chih-Ling Tsai, 2007. "Tuning parameter selectors for the smoothly clipped absolute deviation method," Biometrika, Biometrika Trust, vol. 94(3), pages 553-568.
- Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
- Cai, Tony & Liu, Weidong & Luo, Xi, 2011. "A Constrained â„“1 Minimization Approach to Sparse Precision Matrix Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 594-607.
- Jonas Krampe & Efstathios Paparoditis, 2021. "Sparsity concepts and estimation procedures for high‐dimensional vector autoregressive models," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(5-6), pages 554-579, September.
- Johannes Tang Kristensen, 2017. "Diffusion Indexes With Sparse Loadings," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(3), pages 434-451, July.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
- Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021.
"Factor extraction using Kalman filter and smoothing: This is not just another survey,"
International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
- Poncela Blanco, Maria Pilar, 2020. "Factor extraction using Kalman filter and smoothing: this is not just another survey," DES - Working Papers. Statistics and Econometrics. WS 30644, Universidad Carlos III de Madrid. Departamento de EstadÃstica.
- Catherine Doz & Peter Fuleky, 2019.
"Dynamic Factor Models,"
Working Papers
2019-4, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
- Catherine Doz & Peter Fuleky, 2020. "Dynamic Factor Models," PSE-Ecole d'économie de Paris (Postprint) halshs-02491811, HAL.
- Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," PSE Working Papers halshs-02262202, HAL.
- Catherine Doz & Peter Fuleky, 2020. "Dynamic Factor Models," Post-Print halshs-02491811, HAL.
- Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers halshs-02262202, HAL.
- Pilar Poncela & Esther Ruiz, 2016.
"Small- Versus Big-Data Factor Extraction in Dynamic Factor Models: An Empirical Assessment,"
Advances in Econometrics, in: Dynamic Factor Models, volume 35, pages 401-434,
Emerald Group Publishing Limited.
- Poncela, Pilar, 2015. "Small versus big-data factor extraction in Dynamic Factor Models: An empirical assessment," DES - Working Papers. Statistics and Econometrics. WS ws1502, Universidad Carlos III de Madrid. Departamento de EstadÃstica.
- Smeekes, Stephan & Wijler, Etienne, 2018.
"Macroeconomic forecasting using penalized regression methods,"
International Journal of Forecasting, Elsevier, vol. 34(3), pages 408-430.
- Smeekes, Stephan & Wijler, Etiënne, 2016. "Macroeconomic Forecasting Using Penalized Regression Methods," Research Memorandum 039, Maastricht University, Graduate School of Business and Economics (GSBE).
- Forni, Mario & Hallin, Marc & Lippi, Marco & Zaffaroni, Paolo, 2015.
"Dynamic factor models with infinite-dimensional factor spaces: One-sided representations,"
Journal of Econometrics, Elsevier, vol. 185(2), pages 359-371.
- Mario Forni & Marc Hallin & Marco Lippi & Paolo Zaffaroni, 2012. "Dynamic Factor Models with Infinite-Dimensional Factor Space: One-Sided Representations," Working Papers ECARES ECARES 2012-046, ULB -- Universite Libre de Bruxelles.
- Barigozzi, Matteo & Lippi, Marco & Luciani, Matteo, 2021. "Large-dimensional Dynamic Factor Models: Estimation of Impulse–Response Functions with I(1) cointegrated factors," Journal of Econometrics, Elsevier, vol. 221(2), pages 455-482.
- Jianqing Fan & Kunpeng Li & Yuan Liao, 2020. "Recent Developments on Factor Models and its Applications in Econometric Learning," Papers 2009.10103, arXiv.org.
- Rua, António, 2017.
"A wavelet-based multivariate multiscale approach for forecasting,"
International Journal of Forecasting, Elsevier, vol. 33(3), pages 581-590.
- António Rua, 2016. "A wavelet-based multivariate multiscale approach for forecasting," Working Papers w201612, Banco de Portugal, Economics and Research Department.
- Jin, Sainan & Miao, Ke & Su, Liangjun, 2021.
"On factor models with random missing: EM estimation, inference, and cross validation,"
Journal of Econometrics, Elsevier, vol. 222(1), pages 745-777.
- Su, Liangjun & Miao, Ke & Jin, Sainan, 2019. "On Factor Models with Random Missing: EM Estimation, Inference, and Cross Validation," Economics and Statistics Working Papers 4-2019, Singapore Management University, School of Economics.
- Miao, Ke & Phillips, Peter C.B. & Su, Liangjun, 2023.
"High-dimensional VARs with common factors,"
Journal of Econometrics, Elsevier, vol. 233(1), pages 155-183.
- Ke Miao & Peter C.B. Phillips & Liangjun Su, 2020. "High-Dimensional VARs with Common Factors," Cowles Foundation Discussion Papers 2252, Cowles Foundation for Research in Economics, Yale University.
- Kihwan Kim & Norman Swanson, 2013. "Diffusion Index Model Specification and Estimation Using Mixed Frequency Datasets," Departmental Working Papers 201315, Rutgers University, Department of Economics.
- Mario Forni & Marc Hallin & Marco Lippi & Paolo Zaffaroni, 2011.
"One-Sided Representations of Generalized Dynamic Factor Models,"
Working Papers ECARES
ECARES 2011-019, ULB -- Universite Libre de Bruxelles.
- Mario Forni & Marc Hallin & Marco Lippi & Paolo Zaffaroni, 2011. "One-Sided Representations of Generalized Dynamic Factor Models," DSS Empirical Economics and Econometrics Working Papers Series 2011/5, Centre for Empirical Economics and Econometrics, Department of Statistics, "Sapienza" University of Rome.
- Mario Forni & Marc Hallin & Marco Lippi & Paolo Zaffaroni, 2011. "One-Sided Representations of Generalized Dynamic Factor Models," EIEF Working Papers Series 1106, Einaudi Institute for Economics and Finance (EIEF), revised Mar 2011.
- Bai, Jushan & Liao, Yuan, 2016. "Efficient estimation of approximate factor models via penalized maximum likelihood," Journal of Econometrics, Elsevier, vol. 191(1), pages 1-18.
- Forni, Mario & Cavicchioli, Maddalena & Lippi, Marco & Zaffaroni, Paolo, 2016. "Eigenvalue Ratio Estimators for the Number of Common Factors," CEPR Discussion Papers 11440, C.E.P.R. Discussion Papers.
- Matteo Barigozzi & Matteo Luciani, 2019. "Quasi Maximum Likelihood Estimation and Inference of Large Approximate Dynamic Factor Models via the EM algorithm," Papers 1910.03821, arXiv.org, revised Sep 2024.
- Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2010.
"Are disaggregate data useful for factor analysis in forecasting French GDP?,"
Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 132-144.
- Barhoumi, K. & Darné, O. & Ferrara, L., 2009. "Are disaggregate data useful for factor analysis in forecasting French GDP?," Working papers 232, Banque de France.
- Matteo Barigozzi, 2023. "Quasi Maximum Likelihood Estimation of High-Dimensional Factor Models: A Critical Review," Papers 2303.11777, arXiv.org, revised May 2024.
- Jianqing Fan & Yuan Liao & Han Liu, 2016. "An overview of the estimation of large covariance and precision matrices," Econometrics Journal, Royal Economic Society, vol. 19(1), pages 1-32, February.
- Stock, James H. & Watson, Mark, 2011. "Dynamic Factor Models," Scholarly Articles 28469541, Harvard University Department of Economics.
More about this item
NEP fields
This paper has been announced in the following NEP Reports:- NEP-ECM-2022-01-17 (Econometrics)
- NEP-ETS-2022-01-17 (Econometric Time Series)
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2112.07149. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.